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Considerations when Submitting Proposed Excipient Levels in Inactive Ingredient Controlled Correspondences


    Presenter

    Zakia R. Williams-Greene, PhD
    Senior Pharmacologist, Division of Bioequivalence I (DBI)
    Office of Bioequivalence (OB)
    Office of Generic Drugs (OGD)
    Center for Drug Evaluation and Research (CDER)
    United States Food and Drug Administration (FDA)

    Dr. Zakia Williams-Greene serves as a Senior Pharmacologist in the Division of Bioequivalence I, within the Office of Bioequivalence, Office of Generic Drugs. In this role she assesses bioequivalence studies for various generic drug products, and currently serves as the lead Secondary Assessor for DBI Inactive Ingredient controlled correspondence (CC) reviews. She has been active in numerous Office and Division level committees and working groups and involved in the development and revision of product specific and general guidances.

    Dr. Williams-Greene earned her PhD in Pharmacology from Florida A & M University and her BA in Chemistry with a minor in Spanish from Lincoln University, Pa. She completed post-doctoral training at the National Institutes of Health National Institute on Drug Abuse (NIDA) where she studied stimulant therapy.

    Abstract

    At the 2025 Generic Drugs Forum, Dr. Zakia Williams-Greene presents key considerations when submitting proposed excipient levels in Inactive Ingredient Controlled Correspondence (IIG-CC). She emphasizes aligning Maximum Daily Exposure (MDE) calculations with the Maximum Daily Dose (MDD) from the Reference Listed Drug (RLD) labeling, tailored to specific patient weights or ages. Dr. Williams-Greene outlines the FDA’s recommendation to limit evaluations to three excipients and three proposed levels per inquiry, illustrating common submission pitfalls through five case studies. These examples highlight the importance of selecting appropriate MDDs based on accurate dosing criteria to avoid exceeding the evaluation limit. She concludes by urging applicants to calculate proposed MDEs using specific, corresponding patient characteristics from RLD labeling to streamline evaluations and facilitate timely regulatory feedback.

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